Research on collaborative filtering recommendation algorithm based on user behavior characteristics

The recommendation system of the website can not only recommend products for users and save users the time spent searching for products, but also help practitioners reduce unnecessary sales activities and management expenses. The successful use of the recommendation system can simplify the user's purchase process, improve the user's shopping satisfaction, and can help companies carry out more precise marketing strategies, increase corporate marketing revenue, and increase sales profits. In the context of increasing data volume, the decline in accuracy in the actual recommendation process has caused more and more sales problems. By analyzing and extracting the behavioral characteristics of users visiting the website, this paper proposes a collaborative filtering algorithm based on user behavioral characteristics. Based on the behavioral characteristics shown by the user's page dwell time and number of visits when shopping on the website, the collaborative filtering algorithm is used to find out The shared behavioral characteristics of users during the visit process, recommend potential products of interest to new users, and improve the accuracy of the service and sales of the website. The algorithm can improve the recommendation accuracy and coverage of the recommendation system.